Learning to Evolve on Dynamic Graphs

Representation learning in dynamic graphs is a challenging problem because the topology of graph and node features vary at different time. This requires the model to be able to effectively capture both graph topology information and temporal information. Most existing works are built on recurrent neural networks (RNNs), which are used to exact temporal information of dynamic graphs, and thus they inherit the same drawbacks of RNNs. In this paper, we propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that jointly learns graph information and time information. Specifically, our approach utilizes gradient-based meta-learning to learn updating strategies that have better generalization ability than RNN on snapshots. It is model-agnostic and thus can train any message passing based graph neural network (GNN) on dynamic graphs. To enhance the representation power, we disentangle the embeddings into time embeddings and graph intrinsic embeddings. We conduct experiments on various datasets and down-stream tasks, and the experimental results validate the effectiveness of our method.

DA-HGT: Domain Adaptive Heterogeneous Graph Transformer

Domain adaptation using graph networks is to learn label-discriminative and network-invariant node embeddings by sharing graph parameters. Most existing works focus on domain adaptation of homogeneous networks, and just a few works begin to study heterogeneous cases that only consider the shared node types but ignore the private node types in individual networks. However, for a given source and target heterogeneous networks, they generally contain shared and private node types, where private types bring an extra challenge for graph domain adaptation. In this paper, we investigate Heterogeneous Information Networks (HINs) with partial shared node types and propose a novel domain adaptive heterogeneous graph transformer (DA-HGT) to handle the domain shift between them. DA-HGT can not only align the distributions of identical-type nodes and edges in two HINs but also make full use of different-type nodes and edges to improve the performance of knowledge transfer. Extensive experiments on several datasets demonstrate that DA-HGT can outperform state-of-the-art methods in various domain adaptation tasks across heterogeneous networks.

Hierarchial Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection

This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003;Hengst 2010), and improvements in learning efficiency with heuristic-based subgoal selection and hindsight experience replay (HER)(Andrychowicz et al. 2017; Levy et al. 2019). We propose a new method to integrate HRL, HER and effective subgoal selection based on human expertise to support sample-efficient learning and enhance interpretability of the agent's behavior. Human expertise remains indispensable in many areas such as medicine (Buch, Ahmed, and Maruthappu 2018) and law (Cath 2018), where interpretability, explainability and transparency are crucial in the decision making process, for ethical and legal reasons. Our method simplifies the complex task sets for achieving the overall objectives by decomposing into subgoals at different levels of abstraction. Incorporating relevant subjective knowledge also significantly reduces the computational resources spent in exploration for RL, especially in high speed, changing, and complex environments where the transition dynamics cannot be effectively learned and modelled in a short time. Experimental results in two StarCraft II (SC2) minigames demonstrate that our method can achieve better sample efficiency than flat and end-to-end RL methods, and provide an effective method for explaining the agent's performance.